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Artificial neural networks (ANNs) have become an integral tool in various fields of research. ANNs are mathematical models which can be trained to perform various prediction tasks. The effectiveness of an ANN can be impacted by overfitting which occurs when the ANN overfits to the training data. As a result, the ANN does not generalize well to novel data. In our research, we assess the feasibility of using a chaotic strange attractor to generate sequences of values to inject into an ANN to reduce overfitting. An adaptive method was developed to scale and inject the values into the neurons throughout training. The chaotic injection (CI) was tested on three benchmark datasets using different ANN models. The results were compared against the baseline ANN, dropout (DO), and Gaussian noise injection (GNI). The CI improved the performance of the ANN and converged faster than DO and GNI.2) Professor Kinsner for his guidance while working on my research and coursework.3) Canadian Tire and Mitacs for their support and funding throughout my research.

More than 55,000 people world-wide die from Cardiovascular Disease (CVD) each day. Calcification of the abdominal aorta is an established marker of asymptomatic CVD. It can be observed on scans taken for vertebral fracture assessment from Dual Energy X-ray Absorptiometry machines. Assessment of Abdominal Aortic Calcification (AAC) and timely intervention may help to reinforce public health messages around CVD risk factors and improve disease management, reducing the global health burden related to CVDs. Our research addresses this problem by proposing a novel and reliable framework for automated "fine-grained" assessment of AAC. Inspired by the vision-to-language models, our method performs sequential scoring of calcified lesions along the length of the abdominal aorta on DXA scans; mimicking the human scoring process.